State-of-the-art text simplification (TS) systems adopt end-to-end neural network models to directly generate the simplified version of the input text, and usually function as a blackbox. Moreover, TS is usually treated as an all-purpose generic task under the assumption of homogeneity, where the same simplification is suitable for all. In recent years, however, there has been increasing recognition of the need to adapt the simplification techniques to the specific needs of different target groups. In this work, we aim to advance current research on explainable and controllable TS in two ways: First, building on recently proposed work to increase the transparency of TS systems, we use a large set of (psycho-)linguistic features in combination with pre-trained language models to improve explainable complexity prediction. Second, based on the results of this preliminary task, we extend a state-of-the-art Seq2Seq TS model, ACCESS, to enable explicit control of ten attributes. The results of experiments show (1) that our approach improves the performance of state-of-the-art models for predicting explainable complexity and (2) that explicitly conditioning the Seq2Seq model on ten attributes leads to a significant improvement in performance in both within-domain and out-of-domain settings.

In recent years, there has been increased interest in building predictive models that harness natural language processing and machine learning techniques to detect emotions from various text sources, including social media posts, micro-blogs or news articles. Yet, deployment of such models in real-world sentiment and emotion applications faces challenges, in particular poor out-of-domain generalizability. This is likely due to domain-specific differences (e.g., topics, communicative goals, and annotation schemes) that make transfer between different models of emotion recognition difficult. In this work we propose approaches for text-based emotion detection that leverage transformer models (BERT and RoBERTa) in combination with Bidirectional Long Short-Term Memory (BiLSTM) networks trained on a comprehensive set of psycholinguistic features. First, we evaluate the performance of our models within-domain on two benchmark datasets: GoEmotion and ISEAR. Second, we conduct transfer learning experiments on six datasets from the Unified Emotion Dataset to evaluate their out-of-domain robustness. We find that the proposed hybrid models improve the ability to generalize to out-of-distribution data compared to a standard transformer-based approach. Moreover, we observe that these models perform competitively on in-domain data.

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).

The rapid advancement of AI technology has made text generation tools like GPT-3 and ChatGPT increasingly accessible, scalable, and effective. This can pose serious threat to the credibility of various forms of media if these technologies are used for plagiarism, including scientific literature and news sources. Despite the development of automated methods for paraphrase identification, detecting this type of plagiarism remains a challenge due to the disparate nature of the datasets on which these methods are trained. In this study, we review traditional and current approaches to paraphrase identification and propose a refined typology of paraphrases. We also investigate how this typology is represented in popular datasets and how under-representation of certain types of paraphrases impacts detection capabilities. Finally, we outline new directions for future research and datasets in the pursuit of more effective paraphrase detection using AI.

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.

Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.

即使在高度发达的国家，多达15-30％的人口只能理解使用基本词汇编写的文本。他们对日常文本的理解是有限的，这阻止了他们在社会中发挥积极作用，并就医疗保健，法律代表或民主选择做出明智的决定。词汇简化是一项自然语言处理任务，旨在通过更简单地替换复杂的词汇和表达方式来使每个人都可以理解文本，同时保留原始含义。在过去的20年中，它引起了极大的关注，并且已经针对各种语言提出了全自动词汇简化系统。该领域进步的主要障碍是缺乏用于构建和评估词汇简化系统的高质量数据集。我们提出了一个新的基准数据集，用于英语，西班牙语和（巴西）葡萄牙语中的词汇简化，并提供有关数据选择和注释程序的详细信息。这是第一个可直接比较三种语言的词汇简化系统的数据集。为了展示数据集的可用性，我们将两种具有不同体系结构（神经与非神经）的最先进的词汇简化系统适应所有三种语言（英语，西班牙语和巴西葡萄牙语），并评估他们的表演在我们的新数据集中。为了进行更公平的比较，我们使用多种评估措施来捕获系统功效的各个方面，并讨论其优势和缺点。我们发现，最先进的神经词汇简化系统优于所有三种语言中最先进的非神经词汇简化系统。更重要的是，我们发现最先进的神经词汇简化系统对英语的表现要比西班牙和葡萄牙语要好得多。

法律文本的处理一直是自然语言处理（NLP）的新兴领域的发展。法律文本包含词汇，语义，语法和形态中的独特术语和复杂的语言属性。因此，对于法律领域特定的文本简化（TS）方法的开发对于促进普通人理解法律文本并为主流法律NLP应用程序的高级模型提供投入至关重要。尽管最近的一项研究提出了一种基于规则的TS法律文本方法，但以前尚未考虑法律领域中的基于学习的TS。在这里，我们介绍了一种无监督的法律文本简化方法（USLT）。 USLT通过替换复杂的单词和分裂长句子来执行特定于域的TS。为此，USLT检测句子中的复杂单词，通过掩盖转换器模型生成候选者，并根据等级分数选择替代的候选者。之后，USLT递归将长句子分解为较短的核心和上下文句子的层次结构，同时保留语义含义。我们证明，USLT在文本简单性中优于最先进的域总TS方法，同时保持语义完整。

文本样式传输是自然语言生成中的重要任务，旨在控制生成的文本中的某些属性，例如礼貌，情感，幽默和许多其他特性。它在自然语言处理领域拥有悠久的历史，最近由于深神经模型带来的有希望的性能而重大关注。在本文中，我们对神经文本转移的研究进行了系统调查，自2017年首次神经文本转移工作以来跨越100多个代表文章。我们讨论了任务制定，现有数据集和子任务，评估，以及丰富的方法在存在并行和非平行数据存在下。我们还提供关于这项任务未来发展的各种重要主题的讨论。我们的策据纸张列表在https://github.com/zhijing-jin/text_style_transfer_survey

建模法检索和检索作为预测问题最近被出现为法律智能的主要方法。专注于法律文章检索任务，我们展示了一个名为Lamberta的深度学习框架，该框架被设计用于民法代码，并在意大利民法典上专门培训。为了我们的知识，这是第一项研究提出了基于伯特（来自变压器的双向编码器表示）学习框架的意大利法律制度对意大利法律制度的高级法律文章预测的研究，最近引起了深度学习方法的增加，呈现出色的有效性在几种自然语言处理和学习任务中。我们通过微调意大利文章或其部分的意大利预先训练的意大利预先训练的伯爵来定义Lamberta模型，因为法律文章作为分类任务检索。我们Lamberta框架的一个关键方面是我们构思它以解决极端的分类方案，其特征在于课程数量大，少量学习问题，以及意大利法律预测任务的缺乏测试查询基准。为了解决这些问题，我们为法律文章的无监督标签定义了不同的方法，原则上可以应用于任何法律制度。我们提供了深入了解我们Lamberta模型的解释性和可解释性，并且我们对单一标签以及多标签评估任务进行了广泛的查询模板实验分析。经验证据表明了Lamberta的有效性，以及对广泛使用的深度学习文本分类器和一些构思的几次学习者来说，其优越性是对属性感知预测任务的优势。

数据饥饿的深度神经网络已经将自己作为许多NLP任务的标准建立为包括传统序列标记的标准。尽管他们在高资源语言上表现最先进的表现，但它们仍然落后于低资源场景的统计计数器。一个方法来反击攻击此问题是文本增强，即，从现有数据生成新的合成训练数据点。虽然NLP最近目睹了一种文本增强技术的负载，但该领域仍然缺乏对多种语言和序列标记任务的系统性能分析。为了填补这一差距，我们调查了三类文本增强方法，其在语法（例如，裁剪子句子），令牌（例如，随机字插入）和字符（例如，字符交换）级别上执行更改。我们系统地将它们与语音标记，依赖解析和语义角色标记的分组进行了比较，用于使用各种模型的各种语言系列，包括依赖于诸如MBERT的普赖金的多语言语境化语言模型的架构。增强最显着改善了解析，然后是语音标记和语义角色标记的依赖性解析。我们发现实验技术通常在形态上丰富的语言，而不是越南语等分析语言。我们的研究结果表明，增强技术可以进一步改善基于MBERT的强基线。我们将字符级方法标识为最常见的表演者，而同义词替换和语法增强仪提供不一致的改进。最后，我们讨论了最大依赖于任务，语言对和模型类型的结果。

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

确保适当的标点符号和字母外壳是朝向应用复杂的自然语言处理算法的关键预处理步骤。这对于缺少标点符号和壳体的文本源，例如自动语音识别系统的原始输出。此外，简短的短信和微博的平台提供不可靠且经常错误的标点符号和套管。本调查概述了历史和最先进的技术，用于恢复标点符号和纠正单词套管。此外，突出了当前的挑战和研究方向。

近年来，文本的风格特性吸引了计算语言学研究人员。具体来说，研究人员研究了文本样式转移（TST）任务，该任务旨在在保留其样式独立内容的同时改变文本的风格属性。在过去的几年中，已经开发了许多新颖的TST算法，而该行业利用这些算法来实现令人兴奋的TST应用程序。由于这种共生，TST研究领域迅速发展。本文旨在对有关文本样式转移的最新研究工作进行全面审查。更具体地说，我们创建了一种分类法来组织TST模型，并提供有关最新技术状况的全面摘要。我们回顾了针对TST任务的现有评估方法，并进行了大规模的可重复性研究，我们在两个公开可用的数据集上实验基准了19个最先进的TST TST算法。最后，我们扩展了当前趋势，并就TST领域的新开发发展提供了新的观点。

本文对过去二十年来对自然语言生成（NLG）的研究提供了全面的审查，特别是与数据到文本生成和文本到文本生成深度学习方法有关，以及NLG的新应用技术。该调查旨在（a）给出关于NLG核心任务的最新综合，以及该领域采用的建筑;（b）详细介绍各种NLG任务和数据集，并提请注意NLG评估中的挑战，专注于不同的评估方法及其关系;（c）强调一些未来的强调和相对近期的研究问题，因为NLG和其他人工智能领域的协同作用而增加，例如计算机视觉，文本和计算创造力。

In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing and machine learning techniques. While significant progress has been achieved in this interdisciplinary research area, the vast majority of work has treated MHD as a binary classification task. The multiclass classification setup is, however, essential if we are to uncover the subtle differences among the statistical patterns of language use associated with particular mental health conditions. Here, we report on experiments aimed at predicting six conditions (anxiety, attention deficit hyperactivity disorder, bipolar disorder, post-traumatic stress disorder, depression, and psychological stress) from Reddit social media posts. We explore and compare the performance of hybrid and ensemble models leveraging transformer-based architectures (BERT and RoBERTa) and BiLSTM neural networks trained on within-text distributions of a diverse set of linguistic features. This set encompasses measures of syntactic complexity, lexical sophistication and diversity, readability, and register-specific ngram frequencies, as well as sentiment and emotion lexicons. In addition, we conduct feature ablation experiments to investigate which types of features are most indicative of particular mental health conditions.

基于变压器的语言模型最近在许多自然语言任务中取得了显着的结果。但是，通常通过利用大量培训数据来实现排行榜的性能，并且很少通过将明确的语言知识编码为神经模型。这使许多人质疑语言学对现代自然语言处理的相关性。在本文中，我介绍了几个案例研究，以说明理论语言学和神经语言模型仍然相互关联。首先，语言模型通过提供一个客观的工具来测量语义距离，这对语言学家很有用，语义距离很难使用传统方法。另一方面，语言理论通过提供框架和数据源来探究我们的语言模型，以了解语言理解的特定方面，从而有助于语言建模研究。本论文贡献了三项研究，探讨了语言模型中语法 - 听觉界面的不同方面。在论文的第一部分中，我将语言模型应用于单词类灵活性的问题。我将Mbert作为语义距离测量的来源，我提供了有利于将单词类灵活性分析为方向过程的证据。在论文的第二部分中，我提出了一种方法来测量语言模型中间层的惊奇方法。我的实验表明，包含形态句法异常的句子触发了语言模型早期的惊喜，而不是语义和常识异常。最后，在论文的第三部分中，我适应了一些心理语言学研究，以表明语言模型包含了论证结构结构的知识。总而言之，我的论文在自然语言处理，语言理论和心理语言学之间建立了新的联系，以为语言模型的解释提供新的观点。

深度神经语言模型的最新进展与大规模数据集的能力相结合，加速了自然语言生成系统的发展，这些系统在多种任务和应用程序上下文中产生流利和连贯的文本（在各种成功程度上）。但是，为所需的用户控制这些模型的输出仍然是一个开放的挑战。这不仅对于自定义生成语言的内容和样式至关重要，而且对于他们在现实世界中的安全可靠部署至关重要。我们提出了一项关于受约束神经语言生成的新兴主题的广泛调查，在该主题中，我们通过区分条件和约束（后者是在输出文本上而不是输入的可检验条件），正式定义和分类自然语言生成问题，目前是可检验的）约束文本生成任务，并查看受限文本生成的现有方法和评估指标。我们的目的是强调这个新兴领域的最新进展和趋势，以告知最有希望的方向和局限性，以推动受约束神经语言生成研究的最新作品。

诸如学术文章和商业报告之类的长期文件一直是详细说明重要问题和需要额外关注的复杂主题的标准格式。自动汇总系统可以有效地将长文档置于简短而简洁的文本中，以封装最重要的信息，从而在帮助读者的理解中很重要。最近，随着神经体系结构的出现，已经做出了重大的研究工作，以推动自动文本摘要系统，以及有关将这些系统扩展到长期文档领域的挑战的大量研究。在这项调查中，我们提供了有关长期文档摘要的研究的全面概述，以及其研究环境的三个主要组成部分的系统评估：基准数据集，汇总模型和评估指标。对于每个组成部分，我们在长期汇总的背景下组织文献，并进行经验分析，以扩大有关当前研究进度的观点。实证分析包括一项研究基准数据集的内在特征，摘要模型的多维分析以及摘要评估指标的综述。根据总体发现，我们通过提出可能在这个快速增长的领域中提出未来探索的方向来得出结论。

多文件摘要（MDS）是信息聚合的有效工具，它从与主题相关文档集群生成信息和简洁的摘要。我们的调查是，首先，系统地概述了最近的基于深度学习的MDS模型。我们提出了一种新的分类学，总结神经网络的设计策略，并进行全面的最先进的概要。我们突出了在现有文献中很少讨论的各种客观函数之间的差异。最后，我们提出了与这个新的和令人兴奋的领域有关的几个方向。